Adaptive denoising hyperspectral data for visualization enhancement of intraoperative tissue

Abstract

The vast amount of reflectance information obtained from the hyperspectral imaging devices offers great opportunities for investigating the function and structure of human tissue. However, the captured hyperspectral data often contain various noises due to the intrinsic imperfection of associated electrical and optical imaging components. This work proposed an automatic total variation algorithm to suppress the noises while preserving the details of the spectral and spatial information. The variation of spectral images at neighboring bands was calculated for regulating the total variation of hyperspectral data so that the spectral-dependent noises can be treated differentially across all bands.Experimental results demonstrated that the proposed method could effectively remove the spectral noises, especially near the ends of those extreme bands.The noise suppressed hyperspectral data could then be used for the visualization enhancement on pathophysiological conditions of intraoperative observed anatomies such as the vessels of brain tissues.

In Vitro Antibody Quantification with Hyperspectral Imaging in a Large Field of View for Clinical Applications

“…HSI is a non-invasive, contrast-free tool recently applied in several medical and basic research fields [ 5 , 6 ], like other new emerging technologies [ 7 , 8 ]. Several innovative approaches are under investigation that exploit the capability of HSI to “see beyond” the visible range, such as intraoperative tissue recognition [ 9 ], therapy guidance, and monitoring [ 10 , 11 , 12 , 13 , 14 ].…”